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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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A multi-hop routing protocol for an energy-efficient in wireless sensor network Intisar Shadeed Al-Mejibli; Nawaf Rasheed Alharbe
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2953-2961

Abstract

The low-energy adaptive clustering hierarchy (LEACH) protocol has been developed to be implemented in wireless sensor networks (WSNs) systems such as healthcare and military systems. LEACH protocol depends on clustering the employed sensors and electing one cluster head (CH) for each cluster. The CH nodes are changed periodically to evenly distribute the energy load among sensors. Updating the CH node requires electing different CH and re-clustering sensors. This process consumes sensors’ energy due to sending and receiving many broadcast and unicast messages thus reduces the network lifetime, which is regarded as a significant issue in LEACH. This research develops a new approach based on modifying the LEACH protocol to minimize the need of updating the cluster head. The proposal aims to extend the WSN’s lifetime by maintaining the sensor nodes’ energy. The suggested approach has been evaluated and shown remarkable efficiency in comparison with basic LEACH protocol and not-clustered protocol in terms of extending network lifetime and reducing the required sent messages in the network reflected by 15%, and, in addition, reducing the need to reformatting the clusters frequently and saving network resources.
Context-aware recommender system for multi-user smart home Shymaa Sobhy; Eman M. Mohamed; Arabi Keshk; Mahmoud Hussein
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3192-3203

Abstract

Smart home is one of the most important applications of the internet of things (IoT). Smart home makes life simpler, easier to control, saves energy based on user’s behavior and interaction with the home appliances. Many existing approaches have designed a smart home system using data mining algorithms. However, these approaches do not consider multiusers that exist in the same location and time (which needs a complex control). They also use centralized mining algorithm, then the system’s efficiency is reduced when datasets increase. Therefore, in this paper, we firstly build a context-aware recommender system that considers multi-user’s preferences and solves their conflicts by using unsupervised algorithms to deliver useful recommendation services. Secondly, we improve smart home’s responsive using parallel computing. The results reveal that the proposed method is better than existing approaches.
A sigma-delta interface built-in self-test and calibration for microelectromechanical system accelerometer's utilizing interpolation method Anwer Sabah Ahmed; Qais Al-Gayem
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3367-3374

Abstract

This work presents the capacitive micromechanical accelerometer with a completely differential high-order switched capacitor sigma-delta modulator interface. Such modulation interface circuit generates one-bit output data using a third sigma-delta modulator low-noise front-end, doing away with the requirement for a second enhanced converter of resolution to encode the feedback route analog signal. A capacitive micromechanical sensor unit with just a greater quality factor has been specifically employed to give greater resolution. The closed-loop and electrical correction control are used to dampen the high-Q values to get the system's stability with high-order. This microelectromechanical system (MEMS) capacitive accelerometer was calibrated using a lookup table and Akima interpolation to find manufacturing flaws by recalculating voltage levels for the test electrodes. To determine the proper electrode voltages for fault compensation, COMSOL software simulates a number of defects upon that spring as well as the fingers of the sensor system. When it comes time for the feedback phase of a proof mass displacement correction, these values are subsequently placed in the lookup table.
Design and development of smart interoperable electric vehicle supply equipment for electric mobility Prajeesh C. Balakrishna; Anju S. Pillai
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3509-3518

Abstract

The transportation industry at present is moving towards electrification and the number of electric vehicles in the market increased with the different policies of the directorate. Consumers, who wish to contribute to green mobility are concerned about the limited availability of charging points due to high manufacturing costs and the interoperability issues related to smart charging. This work proposes an Internet of things-based low-cost, interoperable smart electric vehicle supply equipment for deploying in all charging stations. The device hardware is designed to monitor, analyze, and collect consumed energy by the vehicle and transfer this data to a connected network. The pre-defined messages associated with the firmware will help to record this data with a remote management server for further processing. The messages are defined in JavaScript Object Notation (JSON), which helps to overcome the interoperability issue. The device is smart because it can gather energy usage, detect device faults, and be intimate with the controller for a better operational environment. The associated management servers and mobile applications help to operate the smart device remotely and keep track of the usage statics. The developed low-cost, interoperable smart model is most suitable for two and three-wheeler vehicles.
The simulation analysis of stator flux droop minimization in direct torque control open-end winding induction machine Muhammad Zaid Aihsan; Auzani Jidin; Siti Azura Ahmad Tarusan; Tole Sutikno
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3561-3571

Abstract

Direct torque control (DTC) using dual-inverter technique is one of the best topologies for electric vehicle (EV) as it offers abundant selection of voltage vectors to drive the induction machine (IM). This dual-inverter technique also more reassuring as the system still workable even any of its voltage supply is disrupted or the power pack is drained. However, during the uneven voltage supply, the movement of voltage vectors is interrupted and will move obliquely especially in medium voltage vectors. This situation will lead to the faulty movement of the voltage vectors in the default sector definitions and lead to huge flux droop, which later could impose to distort phase current. This paper proposes an optimal sector definition based on the preset voltage ratio between the two inverters. The voltage vectors can be mapped tangentially to the flux vector, minimizing the flux droop and improving the phase current waveform when the proposed sector is utilized. The effectiveness of the proposed sector is tested using MATLAB/Simulink software and the exact parameter from the induction machine.
Intelligent Arabic letters speech recognition system based on mel frequency cepstral coefficients Anas Quteishat; Mahmoud Younis; Ahmed Qtaishat; Anmar Abuhamdah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3348-3358

Abstract

Speech recognition is one of the important applications of artificial intelligence (AI). Speech recognition aims to recognize spoken words regardless of who is speaking to them. The process of voice recognition involves extracting meaningful features from spoken words and then classifying these features into their classes. This paper presents a neural network classification system for Arabic letters. The paper will study the effect of changing the multi-layer perceptron (MLP) artificial neural network (ANN) properties to obtain an optimized performance. The proposed system consists of two main stages; first, the recorded spoken letters are transformed from the time domain into the frequency domain using fast Fourier transform (FFT), and features are extracted using mel frequency cepstral coefficients (MFCC). Second, the extracted features are then classified using the MLP ANN with back-propagation (BP) learning algorithm. The obtained results show that the proposed system along with the extracted features can classify Arabic spoken letters using two neural network hidden layers with an accuracy of around 86%.
Machine learning for internet of things classification using network traffic parameters Loubna Elhaloui; Sanaa El Filali; El Habib Benlahmer; Mohamed Tabaa; Youness Tace; Nouha Rida
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3449-3463

Abstract

With the growth of the internet of things (IoT) smart objects, managing these objects becomes a very important challenge, to know the total number of interconnected objects on a heterogeneous network, and if they are functioning correctly; the use of IoT objects can have advantages in terms of comfort, efficiency, and cost. In this context, the identification of IoT objects is the first step to help owners manage them and ensure the security of their IoT environments such as smart homes, smart buildings, or smart cities. In this paper, to meet the need for IoT object identification, we have deployed an intelligent environment to collect all network traffic traces based on a diverse list of IoT in real-time conditions. In the exploratory phase of this traffic, we have developed learning models capable of identifying and classifying connected IoT objects in our environment. We have applied the six supervised machine learning algorithms: support vector machine, decision tree (DT), random forest (RF), k-nearest neighbors, naive Bayes, and stochastic gradient descent classifier. Finally, the experimental results indicate that the DT and RF models proved to be the most effective and demonstrate an accuracy of 97.72% on the analysis of network traffic data and more particularly information contained in network protocols. Most IoT objects are identified and classified with an accuracy of 99.21%.
Deep learning optimization for drug-target interaction prediction in COVID-19 using graphic processing unit Refianto Damai Darmawan; Wisnu Ananta Kusuma; Hendra Rahmawan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3111-3123

Abstract

The exponentially increasing bioinformatics data raised a new problem: the computation time length. The amount of data that needs to be processed is not matched by an increase in hardware performance, so it burdens researchers on computation time, especially on drug-target interaction prediction, where the computational complexity is exponential. One of the focuses of high-performance computing research is the utilization of the graphics processing unit (GPU) to perform multiple computations in parallel. This study aims to see how well the GPU performs when used for deep learning problems to predict drug-target interactions. This study used the gold-standard data in drug-target interaction (DTI) and the coronavirus disease (COVID-19) dataset. The stages of this research are data acquisition, data preprocessing, model building, hyperparameter tuning, performance evaluation and COVID-19 dataset testing. The results of this study indicate that the use of GPU in deep learning models can speed up the training process by 100 times. In addition, the hyperparameter tuning process is also greatly helped by the presence of the GPU because it can make the process up to 55 times faster. When tested using the COVID-19 dataset, the model showed good performance with 76% accuracy, 74% F-measure and a speed-up value of 179.
Simultaneous network reconfiguration and capacitor allocations using a novel dingo optimization algorithm Samson Oladayo Ayanlade; Abdulrasaq Jimoh; Emmanuel Idowu Ogunwole; Abdullahi Aremu; Abdulsamad Bolakale Jimoh; Dolapo Eniola Owolabi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2384-2395

Abstract

Power loss and voltage magnitude fluctuations are two major issues in distribution networks that have drawn a lot of attention. Numerous strategies have been put forward to provide remedies to lessen the undesirable effects of these issues. Combining two of these approaches and dealing with them simultaneously to get more effective outcomes is essential. Therefore, this study hybridizes the network reconfiguration and capacitor allocation strategies using a novel dingo optimization algorithm (DOA) to solve the optimization problems. The optimization problems for simultaneous network reconfiguration and capacitor allocations were formulated and solved using a novel DOA. To demonstrate its effectiveness, DOA’s results were contrasted with those of the other optimization techniques. The methodology was validated on the IEEE 33-bus network and implemented in the MATLAB program. The results demonstrated that the best network reconfiguration was accomplished with switches 7, 11, 17, 27, and 34 open, and buses 8, 29, and 30 were the best places for capacitors with ideal sizes of 512, 714, and 495 kVAr, respectively. The voltage profile was significantly improved, and the power losses were significantly decreased. When compared to some of the different methods, DOA came out on top.
Optimized design of an extreme low power datalogger for photovoltaic panels Bilal Merabtane; Noureddine Benabadji
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2734-2742

Abstract

The paper focuses on the design and implementation of a low cost and compact data logger prototype using an extreme low power (XLP) and low pin count programmable interface controllers (PIC) microcontroller using its own flash memory for the periodic data acquisition storage, while many other works focus in the Arduino Eco-system. It is planned to pick four important analog measures from the photovoltaic system, and store them directly as 10-bit numerical counts, this yields to faster data acquisition and storage (no time consuming for mathematical computation to convert each numerical count of raw data to meaningful real-world data). Avoiding the use of any kind of display and keypad, and keeping the ratio run time over sleep time as low as possible, has a maximum impact on lowering the power consumption. This prototype can be serially linked to a personal computer (PC) to view the acquisition of measurements in real time, and to retrieve all collected data through a terminal application. The experimental results are stored in comma-separated values (CSV) files to ease post data analysis with any spread sheet software, for statistical calculations and graphs drawing, in order for instance, to find the faults of the photovoltaic system and optimize its management and its performance.

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